Data Engineer – Python, SQL, DBT
Infosys Hyderabad, Telangana, India
Job Description
"Join the league of Data Engineers who shape the future of data-driven decision making with Python, SQL, and DBT expertise at Infosys."
As a Data Engineer at Infosys, you'll be at the forefront of transforming businesses with data-driven insights. With a strong background in Python, SQL, and DBT, you'll design and develop scalable data pipelines that drive business growth.
You'll work closely with cross-functional teams to build cloud-based data solutions using Snowflake, ensuring seamless data integration, quality, and governance.
Why you should learn this:
With the exponential growth of data, the demand for skilled Data Engineers is skyrocketing, with a 14% YoY growth in the market.
Expected Salary: $120,000 - $180,000 per annum, depending on experience and location
How it works:
- Develop ETL/ELT data pipelines using Python and SQL to extract, transform, and load data into data warehouses and lakes.
- Build and maintain DBT transformation models to ensure data quality, validation, and governance.
Core Concepts to Master
Data Warehousing
Design and implement scalable data warehouses using Snowflake, ensuring efficient data storage, processing, and querying.
ETL/ELT Pipelines
Develop ETL/ELT data pipelines using Python, SQL, and DBT to extract, transform, and load data into data warehouses and lakes.
Data Modeling
Design and implement data models to ensure data quality, validation, and governance, using Snowflake's data modeling capabilities.
Performance Optimization
Optimize Snowflake-based data solutions for performance, scalability, and reliability, using CI/CD and DevOps practices.
Interview Questions (Beginner)
- What is ETL/ELT pipeline, and how do you implement it using Python and SQL?
- What is DBT, and how do you use it for data transformation and governance?
- What are the key features of Snowflake, and how do you use them for data warehousing and ETL?
Job Overview
Advance Questions
- • Design a scalable data pipeline using Python, SQL, and DBT to extract data from multiple sources and load it into Snowflake.
- • Implement data modeling and performance optimization techniques to ensure data quality, validation, and governance in Snowflake.
- • Develop a CI/CD pipeline using Airflow, Azure Data Factory, AWS, or GCP to automate data engineering tasks and ensure data reliability.